Changes in processing conditions of a processing (manufacturing) system employed in semiconductor manufacturing can lead to significant loss of revenue due to scrap and non-productive system downtime. In this regard, focus has been placed on system software that monitors operation of the manufacturing system and creates alarms when unacceptable process excursions occur or other fault conditions are encountered.
However, what is needed is a method and system to determine the “health” or comprehensive condition of the processing system on an on-going basis or in real time so as to detect emerging fault conditions. In the past, both system manufacturers and device manufacturers have relied on scheduled preventative maintenance (PM) of the processing system or the occurrence of a catastrophic event. However, the method of using scheduled preventive maintenance is simply based on “rules of thumb” derived from average characteristics, such as mean time between failures (MTBF), and does not address detection, diagnosis, or prediction of faulty conditions for individual processing system components or entire processing systems. In addition, this method does not address gradual degradation or drift in the processing conditions of the processing system.
Traditionally, the cost and bulk of sensing technology means that only a handful of hardwired sensors with little flexibility and networking capability could be deployed for most processing systems. The information collected from the few sensors only provides a relatively small amount of data and does not provide desired real-time monitoring and analysis capabilities needed for comprehensive understanding of the processing condition of the processing system.